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@article{1832899,
author = {Švábenský, Valdemar and Vykopal, Jan and Čeleda, Pavel and Tkáčik, Kristián and Popovič, Daniel},
article_number = {7},
doi = {http://dx.doi.org/10.1007/s10639-022-10954-4},
keywords = {cybersecurity education; security training; data science; educational data mining; learning analytics},
language = {eng},
issn = {1360-2357},
journal = {Education and Information Technologies},
title = {Student Assessment in Cybersecurity Training Automated by Pattern Mining and Clustering},
url = {http://dx.doi.org/10.1007/s10639-022-10954-4},
volume = {27},
year = {2022}
}
TY - JOUR
ID - 1832899
AU - Švábenský, Valdemar - Vykopal, Jan - Čeleda, Pavel - Tkáčik, Kristián - Popovič, Daniel
PY - 2022
TI - Student Assessment in Cybersecurity Training Automated by Pattern Mining and Clustering
JF - Education and Information Technologies
VL - 27
IS - 7
SP - 9231-9262
EP - 9231-9262
PB - Springer
SN - 13602357
KW - cybersecurity education
KW - security training
KW - data science
KW - educational data mining
KW - learning analytics
UR - http://dx.doi.org/10.1007/s10639-022-10954-4
N2 - Hands-on cybersecurity training allows students and professionals to practice various tools and improve their technical skills. The training occurs in an interactive learning environment that enables completing sophisticated tasks in full-fledged operating systems, networks, and applications. During the training, the learning environment allows collecting data about trainees' interactions with the environment, such as their usage of command-line tools. These data contain patterns indicative of trainees' learning processes, and revealing them allows to assess the trainees and provide feedback to help them learn. However, automated analysis of these data is challenging. The training tasks feature complex problem-solving, and many different solution approaches are possible. Moreover, the trainees generate vast amounts of interaction data. This paper explores a dataset from 18 cybersecurity training sessions using data mining and machine learning techniques. We employed pattern mining and clustering to analyze 8834 commands collected from 113 trainees, revealing their typical behavior, mistakes, solution strategies, and difficult training stages. Pattern mining proved suitable in capturing timing information and tool usage frequency. Clustering underlined that many trainees often face the same issues, which can be addressed by targeted scaffolding. Our results show that data mining methods are suitable for analyzing cybersecurity training data. Educational researchers and practitioners can apply these methods in their contexts to assess trainees, support them, and improve the training design. Artifacts associated with this research are publicly available.
ER -
ŠVÁBENSKÝ, Valdemar, Jan VYKOPAL, Pavel ČELEDA, Kristián TKÁČIK and Daniel POPOVIČ. Student Assessment in Cybersecurity Training Automated by Pattern Mining and Clustering. \textit{Education and Information Technologies}. Springer, 2022, vol.~27, No~7, p.~9231-9262. ISSN~1360-2357. Available from: https://dx.doi.org/10.1007/s10639-022-10954-4.
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